06論文筆記《Reducing the Dimensionality of Data with Neural Networks》
通過神經網絡實現降維 簡介： 高維數據通過使用一個小中心層（神經元個數小於維數）的多層神經網絡可以轉換爲低維數據，並可以通過此網絡重建輸入向量。梯度下降法可以用於在這個「自編碼網絡「 調整參數，只是這種機理只在初始權值接近最優參數時有效 。 運用： 特徵降維廣泛應用於分類、可視化、通信和高維數據存儲過程。 創新點： 運用神經網絡進行降維； 概括介紹自編碼器編碼、解碼過程： →編碼→→解碼→ 將原有
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相關文章
- 1. 論文--閱讀翻譯筆記-Reducing the Dimensionality of Data with Neural Networks
- 2. [論文翻譯]Reducing the Dimensionality of Data with Neural Networks
- 3. 論文筆記：Exploring Neural Networks with Activation Atlases
- 4. 論文筆記之Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
- 5. Improved Regularization of Convolutional Neural Networks with Cutout論文筆記
- 6. 論文筆記《DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for 》
- 7. Bag of Tricks for Image Classification with Convolutional Neural Networks論文筆記
- 8. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 9. 【論文閱讀筆記】Imporved Regularization of Convolutional Neural Networks with Cutout
- 10. [論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks